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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets.OpenFinData import OpenFinDataDataset, OpenFinDataKWEvaluator
from opencompass.utils.text_postprocessors import last_capital_postprocess
OpenFinData_datasets = []
OpenFinData_3choices_list = ['emotion_identification', 'entity_disambiguation', 'financial_facts']
OpenFinData_4choices_list = ['data_inspection', 'financial_terminology', 'metric_calculation', 'value_extraction']
OpenFinData_5choices_list = ['intent_understanding']
OpenFinData_keyword_list = ['entity_recognition']
OpenFinData_all_list = OpenFinData_3choices_list + OpenFinData_4choices_list + OpenFinData_5choices_list + OpenFinData_keyword_list
OpenFinData_eval_cfg = dict(evaluator=dict(type=AccEvaluator), pred_postprocessor=dict(type=last_capital_postprocess))
OpenFinData_KW_eval_cfg = dict(evaluator=dict(type=OpenFinDataKWEvaluator))
for _name in OpenFinData_all_list:
if _name in OpenFinData_3choices_list:
OpenFinData_infer_cfg = dict(
ice_template=dict(type=PromptTemplate, template=dict(begin="</E>", round=[
dict(role="HUMAN", prompt=f"{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\n答案: "),
dict(role="BOT", prompt="{answer}")]),
ice_token="</E>"), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer))
OpenFinData_datasets.append(
dict(
type=OpenFinDataDataset,
path="./data/openfindata_release",
name=_name,
abbr="OpenFinData-" + _name,
reader_cfg=dict(
input_columns=["question", "A", "B", "C"],
output_column="answer"),
infer_cfg=OpenFinData_infer_cfg,
eval_cfg=OpenFinData_eval_cfg,
))
if _name in OpenFinData_4choices_list:
OpenFinData_infer_cfg = dict(
ice_template=dict(type=PromptTemplate, template=dict(begin="</E>", round=[
dict(role="HUMAN", prompt=f"{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n答案: "),
dict(role="BOT", prompt="{answer}")]),
ice_token="</E>"), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer))
OpenFinData_datasets.append(
dict(
type=OpenFinDataDataset,
path="./data/openfindata_release",
name=_name,
abbr="OpenFinData-" + _name,
reader_cfg=dict(
input_columns=["question", "A", "B", "C", "D"],
output_column="answer"),
infer_cfg=OpenFinData_infer_cfg,
eval_cfg=OpenFinData_eval_cfg,
))
if _name in OpenFinData_5choices_list:
OpenFinData_infer_cfg = dict(
ice_template=dict(type=PromptTemplate, template=dict(begin="</E>", round=[
dict(role="HUMAN", prompt=f"{{question}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nE. {{E}}\n答案: "),
dict(role="BOT", prompt="{answer}")]),
ice_token="</E>"), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer))
OpenFinData_datasets.append(
dict(
type=OpenFinDataDataset,
path="./data/openfindata_release",
name=_name,
abbr="OpenFinData-" + _name,
reader_cfg=dict(
input_columns=["question", "A", "B", "C", "D", "E"],
output_column="answer"),
infer_cfg=OpenFinData_infer_cfg,
eval_cfg=OpenFinData_eval_cfg,
))
if _name in OpenFinData_keyword_list:
OpenFinData_infer_cfg = dict(
ice_template=dict(type=PromptTemplate, template=dict(begin="</E>", round=[
dict(role="HUMAN", prompt=f"{{question}}\n答案: "),
dict(role="BOT", prompt="{answer}")]),
ice_token="</E>"), retriever=dict(type=ZeroRetriever), inferencer=dict(type=GenInferencer))
OpenFinData_datasets.append(
dict(
type=OpenFinDataDataset,
path="./data/openfindata_release",
name=_name,
abbr="OpenFinData-" + _name,
reader_cfg=dict(
input_columns=["question"],
output_column="answer"),
infer_cfg=OpenFinData_infer_cfg,
eval_cfg=OpenFinData_KW_eval_cfg,
))
del _name
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